"""
Historical Data Reconstruction System

This module provides advanced mathematical techniques for reconstructing complete
personality profiles from sparse historical data. It implements matrix completion,
compressed sensing, temporal evolution modeling, and confidence quantification.

Key Components:
- Matrix completion algorithms for low-rank reconstruction
- Compressed sensing techniques for missing trait inference
- Temporal evolution modeling for personality changes over time
- Bayesian confidence intervals and uncertainty quantification
- Historical reconstructor engine for integrating all techniques
"""

from .matrix_completion import MatrixCompletion, LowRankCompletionAlgorithm
from .compressed_sensing import CompressedSensing, HistoricalSparseRecovery
from .temporal_evolution import (
    TemporalEvolutionModel, 
    PersonalityEvolution, 
    LifeEventImpactModel
)
from .confidence_calculator import (
    BayesianConfidenceCalculator,
    EvidenceCredibilityScorer,
    UncertaintyQuantifier
)
from .historical_reconstructor import HistoricalReconstructor, ReconstructionConfig

__all__ = [
    'MatrixCompletion',
    'LowRankCompletionAlgorithm',
    'CompressedSensing', 
    'HistoricalSparseRecovery',
    'TemporalEvolutionModel',
    'PersonalityEvolution',
    'LifeEventImpactModel',
    'BayesianConfidenceCalculator',
    'EvidenceCredibilityScorer',
    'UncertaintyQuantifier',
    'HistoricalReconstructor',
    'ReconstructionConfig'
]